Trust Rating Metric for Future Event Prediction of an Outcome

ABSTRACT

In at least one embodiment, a trust rating system and method provide a precise and accurate, structured (yet adaptable and flexible), quantifying way of expressing historical trustworthiness so the user or decision maker can make more informed decisions on the data or information being evaluated.

CROSS-REFERENCE TO RELATED APPLICATION

This application claims the benefit under 35 U.S.C. §119(e) and 37C.F.R. §1.78 of U.S. Provisional Application No. 61/321,472, filed Apr.6, 2010, and entitled “Trust Rating Metric for Future Event Predictionof an Outcome Based on Objective Structured Historical Information andTime-Offset Value,” and is incorporated by reference in its entirety.

BACKGROUND

Currently when systems or people try to gauge the trustworthiness ofpredicted data like sales forecasting information, unstructured andsubjective methods are used to reach conclusions. Gut feel andrudimentary surveying of results is often used to develop an opinion asto whether the person or system that provided the predictive forecastdata is trustworthy and whether the data should be acted on or not.These methods do not provide any structure by which repeatedunderstanding of trustworthiness can be analyzed over time.Additionally, these conventional systems, methods, and techniques areextremely subjective. What one person may deem as a high trust may notbe necessarily the same as another.

BRIEF SUMMARY OF THE DRAWINGS

FIG. 1 is an illustration showing an embodiment of a systemarchitecture.

FIG. 2 is an illustration showing an embodiment of a process flow forhow data is moved into the trust rating system and method and how Trustis calculated.

FIG. 3 is an illustration showing an embodiment of how Trust Factor andelements are displayed.

FIG. 4 is an illustration showing an embodiment of a Trust elementweighting is be displayed.

FIG. 5 is an illustration showing an embodiment of an aggregate view ofTrust data for a given category or dimension at a hypothetical level ofRegion in a hierarchy, presented in tabular form.

FIG. 6 is an illustration showing an embodiment of a display of theTrust Factor data of a period of time as a trend.

FIG. 7 is an illustration showing a first example of an embodiment of auser interface (UI) embodiment.

FIG. 8 is an illustration showing a second example of an embodiment of auser interface (UI) embodiment.

FIG. 9 is an illustration showing an embodiment of anAdministration/Configuration user interface (UI) showing one example ofa basic trust setup, expressed as a trust model.

FIG. 10 is an illustration showing an embodiment of anAdministration/Configuration user interface (UI) showing howsegmentation might be presented to a user tasked with setup of thesystem.

FIG. 11 is an illustration showing relationships between a forecaster oranalyst, a first level or direct manager, and a second level orexecutive manager.

FIG. 12 is an illustration of a network environment in which the trustrating system and method may be practiced

FIG. 13 is an illustration of an exemplary computer system in which thetrust factor related system and methods are implemented.

DETAILED DESCRIPTION Introduction

In at least one embodiment, a trust rating system and method provide aprecise and accurate, structured (yet adaptable and flexible),quantifying way of expressing historical trustworthiness so the user ordecision maker can make more informed decisions on the data orinformation being evaluated.

A Trust Factor rating is provided that represents the historicaltrustworthiness of an individual or system that is responsible forgenerating predictive or forecasted data over time. The prediction is anumeric representation of some future happening that is quantifiablelike forecasting the product quantity that might be sold in a futuremonth or perhaps what price it would be sold for. The predictive data isnecessarily followed, at some point in time, by an outcome. As timeprogresses toward the point in time of the outcome the prediction canchange. A lead-time variable is defined to establish the point ofrelevance where-by any further change in prediction is irrelevant to thecomparison to the outcome. The Trust Factor is a compositerepresentation of a number of elements intended to quantify thehistorical trustworthiness of the predictor. The composite notions are,but not limited to, Accuracy, Bias, Completeness and Consistency and areweighted in importance to their overall relationship to trustworthiness.All of which can be derived from the predicted and outcome data overtime.

In at least one embodiment, a computer-implemented trust rating systemand method calculates and provides a trust rating that represents thetrustworthiness of a predictor or forecast. In one non-limiting exampleof the system and method, the system and method may advantageouslyinclude one or a combination of any two or more of the following: (a) afuture measure or outcome being predicted or forecast, (b) the time overwhich the prediction is being measured, (c) the lead-time over which theprediction is valid, (d) the established derived elements of Trust ortrust factor and related weightings, (e) the measured change in thepredicted or forecast values over time, (f) the received outcome dataover time, (g) the calculated or derived elements, (h) the calculatedtrust rating, such as a Trust Factor, as defined by the weighting of thederived elements which may also be displayed, (i) trending informationto tell the trust rating system and method whether the predictor isgetting better or worse at each component over time. The system andmethod may also include a segmentation scheme that allows for definingwhat is deemed untrustworthy or trustworthy in a flexible fashion,and/or an aggregation capability that allows for analysis of the TrustFactor and elements through categories or hierarchies. Other examples ofthe method and system for implementing the method may have fewer ofthese features some of which are optional.

In at least one embodiment, the notion of Accuracy and Trust describedbelow take into account relevant data and how the data changes overtime. For example, in a forecasting system it is not unusual for aparticular forecasted value to change significantly over the course ofsome period of time. Of course, in a simple scenario, a system couldsimply track the current value of the forecast item, but a moresophisticated system and method uses the forecasted value as it existedat some point in the past. In at least one embodiment, simply recordingthe value at the end of some fixed time period, e.g. monthly orquarterly, is inadequate to establish a trust rating, such as a trustfactor or a trust factor rating. Depending on the company or user baseinvolved the specific need of time granularity may change greatlydepending on the problem being addressed. In at least one embodiment,the trust rating system and method have flexibility and configurabilityto accommodate such changes. Referring again to the forecasting example,it may be the case that a forecast value changes on a daily basis but inorder to accurately reflect accuracy, the trust rating system and methodlook at the forecast value 21 days prior to when the outcome isachieved. In at least one embodiment, the trust rating system and methodunderstands and tracks the change at that point in time 21 days prior tooutcome. But if the user changes their need and, for example, requiresthe period to be 23 days prior to outcome, the trust rating system andmethod, in at least one embodiment, is flexible enough to be configuredto track that data to the granularity necessary to handle both a 21 daylook-back and a 23 day look back.

Thus, in at least one embodiment, the trust rating system and methodemploys a delta based system of storing data. Rather than storingcurrent or net values at points in time, the trust rating system andmethod stores the change in the forecast value when that change occursthat achieves the net required. If the user wants to change a value of100 to 110, the trust rating system and method stores the +10 value atthe point in time the change was made. Doing this allows the trustrating system and method to simply add up all the delta values to aparticular point in time that is needed. Most conventional systemsstruggle with this type of requirement. If we take Microsoft Excel™ asan example, each cell in the spreadsheet records the current value so ifit changes it changes for good. In order to preserve what the value wasbefore the change one really needs to save a copy of the spreadsheetbefore the change but then rebuilding “as of” notions becomes cumbersomeand very clumsy. Referring to the present trust rating system andmethod, by storing deltas it is similar in concept to allowing forgranular history of each cell in a spreadsheet and an inherent abilityto go back and reconstruct the spreadsheet as it existed at some pointin the past. This generalized example can be easily extrapolated toconstruct any type of accuracy calculations discussed herein. If thecell is empty it is deemed zero. If a user changes it to 100 on Tuesday,the trust rating system and method records +100 on that day. If the userchanges the value to 110 on Wednesday the trust rating system and methodrecords the +10 and so on. Adding up the deltas up to and includingTuesday yields 100, doing it up and including Wednesday yields 110.

In at least one embodiment, the system and method include a Trust Factorrating that represents the trustworthiness of a prediction. Theprediction is a quantifiable numeric representation of some futureevent, and the prediction can change as the outcome of that eventapproaches. In at least one embodiment, the Trust Factor includeselements that can be derived from the current and past-predicted dataand past outcome data, and each element is weighted in importance andrelevance to the overall notion of trust. Each analyzer may have adifferent notion of what element is more important than the other. Thus,in at least one embodiment, the system and method is capable of allowinga user to weight one or more elements in accordance with an importanceas specified by the user. In at least one embodiment, the system andmethod normalizes each element from past and present data and scoreseach element. Weightings applied to reach a number that can berepresented on a scale, such as a fixed scale, e.g. 0 to 10 representingtrustworthiness. In at least one embodiment, the system and methodfactors in a lead-time notion to the element measurements to define aperiod of time by which the prediction is relevant.

An example usage of the rating system is in the case of a company orcorporate entity that manufactures product to be sold by a group ofsales people. Typically, one or more forecasters are tasked with the jobof forecasting the quantities and prices for sales of product in thefuture. This prediction can be made some time in the future of the saleactually happening. The prediction can precede the event anywhere from afew months in advance to years. In at least one embodiment, when gaugingthe trustworthiness of the forecaster, the system and method evaluatehow successful the prediction is, taking into account the fact thatproducts typically take time to build. If it takes 6 weeks to build itis advantageous to have a good prediction of the sales outlook at thattime. If the trust rating system and method assesses trustworthiness,this lead-time should be factored in so that when the trust ratingsystem and method or a user measures the trustworthiness of theprediction, the trust rating system and method assesses trustworthinessat the time before a commitment to action on that prediction. As such,when trying to quantify trust, the trust rating system and methodcalculates its meaning at that time. For a generic solution, in at leastone embodiment, the system and method are capable of configuring thisability from customer to customer or from prediction to predictionusing, for example, user input data.

In addition, there are different factors that can contribute to anobjective notion of trustworthiness. Accuracy, bias, consistency andcompleteness are among them but different situations may call for theaddition of new ones or the omission of others. Some additional factorsused for calculation could in fact be external to the system altogethersuch as exchange rate information or perhaps weather forecasts.

To extend the example, trustworthiness in a forecasting process canmanifest itself in a feedback process in a risk assessment scenario asillustrated by the process flow in FIG. 11. Here are three roles, theforecasters who enter the base predictive data, the manager who vetstheir forecasts and the Vice President or Executive that oversees andcommits the forecast to the company. In this example, the process is asfollows:

Step 1201—Forecaster submits prediction to the manager as a forecast forspecific Products and Customers but other categories may be necessary,it depends on the type of forecast being collected

Step 1203—Manger reviews the forecast information and changes that havebeen made.

Step 1204—Manager overlays the Trust Factor information and associatedelements and analyzes the forecast revenue looking for areas that have alow trust score. This represents risk in the forecast.

Step 1205—If the forecast is high risk and the associated revenue islarge the manager may not approve this scenario. If this is not the casethe manager can submit the forecast to the Executive at 1208.

Step 1206—if the forecast is deemed to contain too much risk the managercan either adjust the forecast with his or her own judgment or requestthat a forecaster adjust the forecast based on the feedback givenregarding the risk profile observed.

Step 1202—if the manager rejects the forecast back to the forecaster,the forecast can be updated accordingly based on the feedback from 1206and resubmitted via 1201.

Step 1207—if the manager assumes the responsibility for changing theforecast, they can enter their own judgment as it relates to theobserved risk in the forecast.

Step 1208—at this point the manager submits the forecast to theExecutive which in some regards could be seen as a commitment to theforecast.

Step 1209—the executive reviews the forecast in much the same way as themanager albeit probably at a less detailed level digesting both theforecast values but also taking into account the trust information andperceived risk.

Step 1210—in some cases the forecast may be deemed at this level to beunacceptable either because the forecast falls short of expectation orthat the risk profile as highlighted by the trust information isunacceptable.

Step 1211—based on executive feedback, the forecast can be updated againby the manager and the feedback process between the manager and theforecaster can be repeated.

Therefore it will be apparent that in one example of a process accordingto an example of the invention, there is a method that includes thesteps (some optional) of: (A) receiving (e.g., by a computer or a servercomputer) at least one initial future forecast prediction data from anexternal forecaster for a future event or condition that has not yetoccurred;

(B) communicating the at least one initial future forecast predictiondata to a first level manager, and optionally communicating any changesto the at least one initial future forecast prediction data to the firstlevel manager, the first level manager being at a higher responsibilityrelative to the forecaster and an executive level manager at a higherresponsibility relative to the first level manager;

(C) reviewing the at least one initial future forecast prediction data(e.g., by the first level manager);

(D) overlaying (e.g., by the first level manager) a trust factorinformation and associated elements or components;

(E) analyzing the at least one initial future forecast prediction dataand any changes to the initial future forecast prediction data (such asforecast revenues or sales or other event outcome) to identify values,predictions, and/or forecasts that have a trust factor score that has amagnitude relationship relative to a predetermined trust factor score(or a trust factor score that is too low or below a predetermined ordynamically determined trust factor score threshold) that indicate aforecast risk (or a forecast profile) in the future forecast predictiondata;

(F) determining if the identified risk is greater than or less than arisk threshold, the risk threshold optionally being adjusted ordynamically determined based on an impact on an associated performancemetric (or revenue or sales metric;

(G) if the forecast risk is less than the risk threshold in view of theimpact of an inaccurate forecast, then approving the forecast andcommunicating the forecast to an executive manager for furtherconsideration, but

if the risk is greater than the risk threshold in view of the impact ofan inaccurate forecast, then either

(i) sending the forecast back to the forecaster with first level managerfeedback comments and a request that the forecast be updated based onthe first level manager feedback; or

(ii) the first level manager updating/revising the forecast based on thefirst level manager's judgment (in combination with other facts andknowledge or research);

(H) when the forecast is sent back to the forecaster forupdate/revision, receiving an updated or revised forecast in response tothe first level manager feedback and request for update/revision;

(I) when the forecast is updated/revised by the first level manager,receiving an updated or revised forecast from the first level manager,the updated/revised forecast being either a change in the forecast or acomment attached to the forecast to explain/justify the forecast and therisk, or a combination of a change in the forecast and a commentattached to the forecast to explain/justify the forecast;

(J) iteratively revising the forecast by the forecaster and/or the firstlevel manager until the risk based on trust is determined to be withinacceptable limits, and then communicating the forecast including anyupdates/revisions to the forecast to the executive manager, thecommunicating of the trust factor reviewed and possibly updated/revisedforecast to the executive manager representing a commitment of the firstlevel manager to the forecast;

(K) analyzing the committed forecast (e.g., by the executive manager)using the trust factor, including reviewing the forecast value(s), thetrust information, and the identified risk;

(L) determining that the committed forecast is unacceptable for areason, the reason being one or a combination of two or more selectedfrom (i) the forecast is not within an acceptable forecast value rangefor the forecast event (or the forecast falls short of expectations),(ii) the risk profile (as highlighted by the trust information) isunacceptable;

(M) if the committed forecast from the first level manager isunacceptable, then communicating the forecast with executive feedbackincluding executive manager feedback comments, to the first levelmanager for updating/revision by the first level manager and/or forupdating/revision by the forecaster; and

(N) if the committed forecast from the first level manager is acceptablethen identifying the executive manager approved forecast as the finalforecast that has passed the trust factor analysis with acceptable trustrisk and acceptable performance metric.

Specialize processing engines may be provided and configured toimplement each of the above described process steps, either alone or incombination within a single or smaller set of engines.

Another example is evaluating analyst's predictions regarding stocks. Itis more advantageous to know or have a sense of what a stock orfinancial instrument is going to do in advance of a particular date. Themore of a horizon you have, the greater the advantage. When gauging thetrustworthiness of a stock predictor a user or the trust rating systemand method can do so at different lengths of time. In at least oneembodiment, different notions contribute to the overall trustworthinessthat are different than, for example, sales forecasting. It may be thecase that only accuracy of the prediction and bias of the prediction areimportant while consistency and completeness are not. It may also be thecase that addition notions be factored into the notion oftrustworthiness. In at least one embodiment, this flexibility exists inthe solution.

It may be appreciated in light of the description provided herein thatthe manner in which the trust factor and/or its individual or combinedcomponents may trend over time may also be valuable or provide valuableinput or insight for forecasting. For example, the evolution or behavioror trending of the trust factor over a period of time for a particularanalyst/predictor or combination of predictor/analyst and product, mayidentify an optimum or near optimum, or at least really good predictionsituation or sweet spot, where the prediction might be expected to bequite valuable. For example, it may be valuable in that it has highaccuracy or is valuable respective of one of the other trust factorcomponents, or combination of components.

Management might for example be able to look at trust factors andpredictions or forecasts for a particular analyst (or set of analysts)where the predictions or forecasts were may every week with lead times(time in advance of the forecast event) of 6-weeks, 5-weeks, 4-weeks,3-weeks, 2-weeks, 1-weeks, and identify one particular analyst as beingparticularly trust worthy at 1-3 weeks, but not as trustworthy forperiods of time greater than 3 weeks; and, identify a second particularanalyst that is particularly good and trust worthy at forecasting at 4-8weeks in advance of the event, but not particularly good or just averageat more near term predictions. Other forecasters, predictors, oranalysts may also outperform others for particular forecastingscenarios. These outperforming predictors or analysts might be productor industry specific. These outperforming predictor analysts may alsohave other factors that need to be taken into account, such as ananalyst or predictor that has a very trustworthy record for yearendpredictions but average or below average the rest of the year.

Looking at these trends or performance indicators would be relevant topredicting all sorts of events or non-events, even includingmanufacturing or supply events, and even including manufacturingsituations where manufacture of an article needs to commence in a timeframe outside of the outperforming predictor is accurate, but within amanufacturing scenario where the manufacturing timelines or quantitiescan be modified as more trustworthy predictions become available.

For example in a manufacturing company making widgets, even though thelead-time for manufacturing the widgets is 6 weeks, one may want to lookat the subsequent Trust Factor scoring for predictors that are accurateat periods inside the 6 weeks lead time (for example, within the 1-3week time frame). This may allow for a more informed exception processin that even though one is inside the manufacturing lead-time, one maywant to adjust plans and incur additional costs based on a highlytrustworthy adjusted forecast, such as to add another production shiftor to scale back the production so as not to overproduce widgets thatcannot be sold.

As another example a person who predicts the over and under of asporting event score could be scored for trustworthiness in a similarway. The more in advance of the sporting event you can gauge theoutcome, the better advantage you can take of the odds established onthe outcome. When looking at the historical trustworthiness of thepredictor it may be necessary to calculate the notions of accuracy andwhatever else is deemed important several weeks prior to the event. Assuch, the trust rating system and method or a user can determine a moreobjective score as to whether the predictor is historically trustworthyor not. Again, this notion of time in advance of event may need to beconfigurable based on the type of event being measured.

The trust factor is not limited in its application to only persons doingthe prediction. In at least one embodiment, it may be important tofurther dissect the prediction and look at the calculated trust factorfor what the prediction is for. For example, if a forecast isforecasting products it may be that the forecasted overall trust factoris good but upon inspection, it could be that the trust factor forproduction of two certain products is poor but the aggregation of allthe predictions hid that notion. Thus, in at least one embodiment, thetrust rating system and method is capable of dissecting or slicing thetrust factor or reverse aggregate.

In at least one embodiment, the trust rating system and method comparesand contrasts the trust factor ratings. In at least one embodiment, theapplication of the trust factor in the trust rating system and methodallows for the comparison of elements in the system. It could be assimple as forecaster A compared to forecaster B or perhaps as complex ascomparing forecaster A to the top 10 of other forecasters in the systemusing the trust factor as the scoring mechanism.

In at least one embodiment, the trust factor is a metric that canpermeate the system itself or be used outside of the system in that whenitems are displayed (for example on a monitor) for which a trust factorcould be calculated, it may be advantageous to show the trust factor andrelated components for that item in conjunction with what is beingdisplayed. By example, in a forecasting system for sales forecastingdata, typically the user is presented with tools to manage things likeproducts and regions and the users or forecasters. In at least oneembodiment, the trust rating system and method causes the related trustfactor to be displayed in those screens as well as contextualinformation. In addition, it is not unusual for forecasting systems ofthis nature to be integrated with or at least compliment other systemslike Customer Relationship Management system. Thus, in at least oneembodiment, the trust rating system and method provides the trust factorinformation to those systems so that a user may see the trust factordata when viewing these items in those systems as well.

Overview of the Trust Factor Rating

The Trust Factor of a prediction may be a measure or metric such as anumerical value between a first rating range value and a second ratingrange value. Conveniently, the trust factor may be a value between 0 and10 (or between 1 and 10) or any other scaled range for such a value. Itmay be a linear scale or a non-linear scale. The trust factor indicatesthe calculated historical trustworthiness of a system or persongenerating numeric predicted values over time. 10 would represent thehighest level of historical trust while 0 would represent the lowest.The Trust Factor is generated periodically as the measured outcomes ofthe predictions are recorded. The Trust Factor is a value derived from aweighted group of sub-measurements called the elements of trust. Some ofthese elements of trust use a notion of lead time or offset to definethe period by which changes to the prediction are relevant leading up tothe point where the outcome is captured. An example displaying of theTrust Factor rating can be seen in FIG. 3.

Some non-limiting examples of candidate elements or components of thetrust factor are now described. Some of the described elements areoptional. In one example, the Trust Factor is a derived from thenormalization and weighting of a number of calculated measurements thatare derived from the predicted and outcome data. These elements aredefined by the user analyzing the trustworthiness and can be extended toinclude additional element calculations. For convenience, the basicelements are, but not limited to, accuracy, bias, completeness andconsistency. In many cases time plays a part in these elements. Anoffset value for time is used by the trust rating system and method tohandle this.

An example of these elements as potentially displayed can be seen inFIG. 3.

Time Offset Value is a concept that pertains to the length of time inadvance of an event or actual outcome that a forecast or predictionabout the outcome is made. An accurate prediction or forecast that ismade well in advance (for example 3 months in advance) of an outcome isin principle more important and valuable than an equally accurateprediction that is made a shorter period of time (for example 1 week inadvance) in advance of the outcome. It may even be true that a somewhatless accurate outcome made well in advance of the event or outcome ismore valuable than a more accurate prediction made a shorter period oftime in advance. For example, if the actual outcome is 20 units, and theprediction made 3 months in advance was 17 units but the outcomepredicted 1 month in advance is 18 units, the earlier prediction mayhave more value and utility even though it is less accurate. The idea ofthe time offset value is to give the trust factor more credibility orhigher weighting or meaning when a forecaster predicts accurately alonger period of advance of the event or outcome.

In at least one embodiment, the trust rating system and method allowsfor a certain level of trust factor modeling whereby the trust ratingsystem and method are capable of allowing for the use of different trustfactor calculations at different points in time or different offsets.The previous paragraph opens up the possibility that this could beuseful in that the perception of trust is somehow different at certainpoints in time. The notion that lower accuracy at a longer horizonsuggests perhaps that the system could use some kind of normalization sothat the trust factor calculation takes this into account.

It may also be advantageous to include factors that take into accountmultiple Time Offsets for trending and other types of analysis. In thiscase the inventive system and method may advantageously include factorsthat provide for inclusion of predictor velocity and predictor directionof change measures or metrics over periods of time. Relevant analysismay then take into account how quickly do they achieve a maximum oroptimum Trust Factor, how long after the optimum Lead Time is themaximum Trust Factor achieved, and/or what is the minimum Trust Factorover a range of Lead Times?

When dealing with predicted data that, in at least one embodiment,invariably implies an outcome, the notion of time and its relationshipto this data should be understood.

An example is now described. A sales person predicts in January that 10units of product x will be sold in May. This is a prediction 5 monthsprior to the outcome. It may be the case that in February this insightchanges and the prediction now become 20. Time continues to progress andon the last day of April the prediction is changed to 15 and on the nextday the sales was made and the outcome was in fact 15. Was thisindividual accurate?

In most cases there is a lead-time to react to changes in prediction. Inthis case, for example, product x takes 2 weeks to make so in order tojudge a prediction as accurate or not it may be necessary to take theprediction as it existed 2 weeks prior to the outcome to determine this.

In the example this would have in fact been 20 and not 15 making thesales person somewhat inaccurate in the prediction.

This is what is to be known as the time offset value. It represents aperiod of time, such as a period of days, weeks, months, or years (orany other period of time or delay between the forecast or prediction andthe event outcome) prior to the recording of the outcome by whichcertain trust measurements should be calculated. It is a configurablevalue and in many cases different things utilize different offsets orperhaps multiple offsets for deeper analysis. It may be the case for thesame forecaster that a second product, product y, has a lead-time of 4weeks because it takes that much longer to build. In at least oneembodiment, when calculating an aggregate trustworthiness of thisforecaster for the two products this dual notion of offset should beunderstood.

Accuracy represents variance comparison of the prediction to the outcomevalue. By example, if a sales person predicts that in May they will sell10 units of product x and when may arrives they actually did sell 10units. They are 100% accurate. Any variance to the outcome however canbe calculated as an absolute value departure away from 100%. Should theoutcome have been 9 they could be said to have been 90% accurate. Inother words they achieved a lower calculated accuracy. There are manypossible metrics for accuracy but basically, accuracy is a differencebetween the actual outcome and the predicted or forecast outcome. It maybe scaled or normalized in a number of different possible ways.Furthermore, it may or may not matter if the predicted value is higheror lower than the actual outcome. If the positive or negative nature(higher or lower) is not an issue, then the absolute value (ABS) ormagnitude of the difference may be utilized. If knowing and taking intoaccount that the forecaster or forecast is frequently over estimating orunderestimating, then the sign of the difference may be included in theaccuracy component calculation.

In one non-limiting example, the accuracy may be represented by: senseaccuracy is:

Accuracy=1−ABS((P−O)/(P+O)),

where P is prediction, O is outcome.

Of course, this particular non-limiting example does not factor in thenotion of the time-offset value as does an alternative example describedelsewhere herein. If lead-time (or equivalently time offset) is to befactored in to this notion then the formula should also look at the timeoffset value for the value (P_(tov)) of the prediction. In this case theformula changes to look like this:

Accuracy=1−ABS((P _(tov) −O)/(P _(tov) +O)),

where tov represents the time-offset value, and Ptov represents thePrediction at the tov time.

Bias may represent another trust factor element or component, and mayoptionally be used or not used in the trust factor generation. Whereaccuracy represents the difference between the prediction and outcome atsome point in time it has no interest in understanding the direction ofthe difference. In other words, whether the difference is positive ornegative does not really have an impact to the notion.

Bias on the other hand, is intended to quantify this notion of whetherthe difference between the recited outcome and the actual outcome ispositive or negative. Whether a forecaster tends in their prediction tobe overly optimistic or pessimistic is advantageously to be factored in.Referring to the basic example, in the case where the prediction is 11and the outcome is 10 the forecaster was biased +10%. Alternativelywhere 9 was predicted they can be deemed to be −10% biased.

In one sense bias may be represented by:

Bias=1−((P−O)/(O)),

where P is prediction value, O is outcome value for the same event asthe prediction.

Of course, this does not factor in the notion of the time-offset value.If lead-time is to be factored in to this notion then the formula, in atleast one embodiment, includes the offset value for the value of theprediction. In this case the formula changes to look like this:

Bias=1−((P _(tov) −O)/(O)),

where tov represents the time-offset value.

It stands to reason that if the offset value is used for accuracy it ismore than likely to be factored in for bias but this may in fact notalways be the case. That said the system could track the offset for eachindependently if necessary.

Completeness, may be a further (optional) trust factor element orcomponent, and measures the amount of forecast activity within a certaintime frame. In actuality, completeness concerns itself with thepredicted data within a certain bounded period of time. For example, ifyou have 10 things you are trying to predict 6 months prior to theoutcome you may look at the number of changes or acknowledgments thathave been made in say, the most recent 30 days to derive a completenessmeasurement. In this case, acknowledgement is the act of committing thata forecasted value is still valid without changing the prediction. Ifthe forecaster touches 2 of the possible items then they are deemed tobe 20% complete for the 30 day window.

Assuming that for the trust rating system and method to measuretrustworthiness, there is a universe of possible things that can bepredicted and a time over which they are predicted for. For example, aforecasting system may be forecasting revenue for a given set ofproducts, sold to a given number of customers over a possible number ofmonths. At any point in time a forecaster may either change theirprediction or acknowledge that the prediction is still valid.

Attainment may be another optional trust factor element or component. Atthe point upon reaching the horizon of the time offset for a prediction,another notion can be derived called attainment. Attainment representshow close the prediction actually was to the outcome. For example, if atthe time offset point, a sales person predicts that they will sell 100units of product x in May and in actuality, when May arrives, they sold120 units, you could say that they achieved 120% attainment. That said,attainment can be expressed as:

Attainment=(O/P _(tov))×10,

where P is prediction, O is outcome and tov represents the time-offsetvalue.

Consistency may be another optional trust factor element or component.Consistency is an expression of attainment and its oscillation overtime. A consistent predictor is a person or system that consistentlyachieves a low standard deviation over a sample set of attainmentmeasurements. For example, Table 1 relates to a sale person forecastingproduct sales and has been doing so over a 4-quarter period:

TABLE 1 Prediction Outcome Attainment Baseline Qtr 1, 09 1100 100090.91% 100.00% Qtr 2, 09 1350 1200 88.89% 100.00% Qtr 3, 09 1500 140093.33% 100.00% Qtr 4, 09 1250 1200 96.00% 100.00%

The trust rating system and method calculates the attainment in eachperiod as described previously.

As expected, this visualization represents a fairly consistentforecaster. The standard deviation of this data sampling is in fact3.07%. The following table represents an alternative:

TABLE 2 Prediction Outcome Attainment Baseline Qtr 1, 09 1550 100064.52% 100.00% Qtr 2, 09 700 1200 171.43% 100.00% Qtr 3, 09 2000 140070.00% 100.00% Qtr 4, 09 2000 1200 60.00% 100.00%

It should be clear from above that this forecast is inconsistent andthis is reflected a standard deviation of 53.45%.

It may also be appreciated that the inventive system and methodcontemplates various ways of expressing consistency of data recordedover periods of time and the above examples merely outlines one possibleway of doing this.

Normalizing the Elements. One of the steps in taking the trust factorelements or components and composing or combining them into the TrustFactor is to normalize the measured results. Normalizing is advantageousbut not required in all embodiments of the inventive system and method.When it is done, it can be done in a few ways depending on the type ofelement representation that the trust rating system and method dealingwith.

Absolute Capped Percentage. This is by far the easiest to normalize. Ifthe trust rating system and method knows the percentage from 0 to 100,simple division by 10 will condense the scale to what is relevant to theTrust Factor. Examples of this are Accuracy and Completeness.

Unbounded Positive and Negative Percentage. In some cases the derivedmeasure for the element cannot be boxed in to a fixed range and in factcan be represented as both positive and negative. In this case the rangemapping schema is useful to condense the possible values down to anumeric value between 0 and 10. For example, suppose if the Bias can befrom +/−0 to infinity in its percentage representation, the trust ratingsystem and method determines how that is represented as a 0 to 10 valueby defining the ranges for each. The table below (Table 3) shows onesuch mapping.

Once the mapping is done and the mapped value is calculated the absolutevalue is taken to arrive at our value between 0 and 10. It should benoted that this allows for different mapping for the positive andnegative values. It could be the case the −80 percent is mapped to anabsolute value of 4 while +80% is mapped to a value of 2.

It can also be the case that for an uncapped positive percentagemapping, as is the case with Consistency, the trust rating system andmethod would simply not map the negative values at all since they are,in at least one embodiment, impossible for the element.

TABLE 3 Range Mapped Value >100% 0 90% to 99% 1 80% to 89% 2 70% to 79%3 60% to 69% 4 50% to 59% 5 40% to 49% 6 30% to 39% 7 20% to 29% 8 10%to 19% 9    0 to 9% 10 0% to 9% −10 −10% to −19% −9 −20% to −29% −8 −30%to −39% −7 −40% to −49% −6 −50% to −59% −5 −60% to −69% −4 −70% to −79%−3 −80% to −89% −2 −90% to −99% −1

Weighting the Elements

Now the trust rating system and method has established the multipleelements of trust taken each and correspondingly derived a value from0-10 for each. In determining the overall Trust Factor for any giventhing, in at least one embodiment, various elements are weighted. Forexample, with regard to trust, elements are weighted in accordance withtheir relative importance with reference to each other. This may veryfrom one analyzer to the other and so it is such that this isconfigurable.

By example, if there are 4 trust elements, Accuracy, Bias, Completenessand Consistency, a percentage value is assigned by, for example a userand entered into the trust rating system and method, to each elementsuch that the sum of the percents adds up to 100. This effectivelyweights each in terms of importance.

TABLE 4 Element Percent Weight Accuracy (Ac) 50% (or 0.50 weight) Bias(Bi) 30% (or 0.30 weight) Completeness (Cm) 10% (or 0.10 weight)Consistency (Cn) 10% (or 0.10 weight)

In the above example of a particular understanding of trustworthiness,Accuracy and Bias are heavily weighted. The Trust Factor would then be:

Trust Factor=(Ac×0.5)+(Bi×0.3)+(Cm×0.1)+(Cn×0.1)

More generally:

Trust Factor=Function1{Ac,W _(Ac) ,Bi,W _(Bi) ,Cm,W _(Cm) ,Cn,W _(Cn)},

where Function1 is some function of the variables, constants, or othervalues or parameters enclosed within the { } brackets.

In one non-limiting example, the function is a multiplication betweenthe trust element and the element weighting, and these weighted trustelements are combined in accord with a second functional relationship asfollows:

Trust Factor=Function2(Ac×W _(Ac)),(Bi×W _(Bi)),(Cm×W _(Cm)),(Cn×W_(Cn))},

where Function2 is some function of the variables, constants, or othervalues or parameters enclosed within the { } brackets.

In one non-limiting example, the function may combine the four elements,or any combination of one or more elements, in a linear or non-linearmanner, and the manner that they are combined may be different for thedifferent elements. In one particular example, they may be added alongwith the Constant multipliers that may differ between the differentelements, and represent an additional weighting.

When the elements are combined by addition, the relationship becomes:

Trust Factor=C ₁×(Ac×W _(Ac))+C ₂×(Bi×W _(Bi))+C ₃×(Cm×W _(Cm))+C₄×(Cn×W _(Cn))

Where:

C₁=is a first constant that may be any real number

C₂=is a second constant that may be any real number

C₃=is a third constant that may be any real number

C₄=is a fourth constant that may be any real number

Ac=Accuracy element

W_(Ac)=Numerical Weighting of the Accuracy element

Bi=Bias element

W_(Bi)=Numerical Weighting of the Bias element

Cm=Completeness element

W_(Cm)=Numerical Weighting of the Completeness element

Cn=Completeness element

W_(Cn)=Numerical Weighting of the Completeness element

Note that any of these weightings may be zero (“0”) or zero percent.

If the weightings, constants, and manner of combination are chosenappropriately, then the trust factor may have a resultant numericalvalue that is between first and second limits. In one non-limitingexample, the resulting value is a weighted value between 0 and 10 thatrepresents the Trust Factor for our predicted values. By setting apercent weight to zero, the element is effectively out of playaltogether which may be a desired outcome.

It may also be appreciated that in non-limiting examples, it may beadvantageous to allow for different things to have different weightingschemes. For example, in a situation where there are two differentproduct lines. One of the two lines may be naturally inconsistent overtime, and the factors that influence the first product line are notsusceptible to consistent prediction. On the other hand, the secondproduct line is much more consistent naturally. It may be advantageousto weight consistency differently for Trust Factor calculations on eachline independently. This differentiation may be true for a variety ofdifferent prediction scenarios whether products, services, marketbehavior, or any other event.

Visualization of the weighting is advantageous, but optional, and whenprovided is advantageous both for initial setup of the application aswell as through analysis of the actual trust information. The manner ofgenerating the visualization and data that supports it as well as theinterface are additional features of the technique described here andadvances over conventional systems and methods.

FIG. 9 shows an example of the weighting percentages as potentiallydisplayed in a setup UI when a “Trust Model” or configuration is to beestablished. FIG. 4 depicts the percentage weighting displayed back to auser for context.

Separate to the scoring and weighting of the elements it is criticalthat each be trended. Taking consistency as an example, if a predictoris inconsistent it would be good to know whether they are at leastgetting better or worse. This can be achieved by looking at the relativeslope of the consistencies normalized score. Taking the slope, anothermapping can be introduced to, for example, present the trend in simpleterms such as: Slowly getting better, Quickly getting better, neutral,quickly getting worse, slowly getting worse. An example of how thiscould be displayed in the application can be seen in FIG. 6 where a 4period historical trend of the Trust Factor as well as the Accuracyelement is portrayed.

Additionally, in at least one embodiment, the trust rating system andmethod use Trust Factor trending to forecast and ascertain thetrustworthiness of the trust factor itself. In at least one embodiment,the projected trust factor for the current point in time is as anotherelement for providing judgment. For example, if a forecaster is trendinga certain way, the trust rating system and method can, in at least oneembodiment, calculate and display the trend and an associatedtrustworthiness of the forecaster.

Aggregation

In a predictive system it is generally a good idea to organize predictedvalues into categories to make it easier to capture the information in ameaningful way. By example, in a forecasting system forecast data isadvantageously captured along the lines of Customers, Products and/orRegions or combinations of these but not necessarily limited to justthose. One term that may be used to describe these categories isdimensions. Organizing the forecasted data into dimensions that may ormay not be represented as hierarchies is advantageously used in aforecasting system. When data can be represented as hierarchies andtrust or trust factor is applied, and in one example of the system andmethod, the trust data and trust factor (and/or its component elements)can aggregate up the hierarchies for the purpose of analysis.

For example, for a Regional dimension for which forecast data iscaptured, it may be the case that Sub-regions roll up into Regions. Inthis case it is advantageous to be able to look at the Trust Factorrating and associated elements not only at the lower Sub-region levelbut also at an aggregate Region level as well. FIG. 5 shows a list likeview as an example where by the Regional information is displayed as anaggregate view of the children of the Regions dimension or hierarchy.

Segmentation

Once data is normalized as mentioned earlier, it may be advantageous tosegment the values and assign color representations in a meaningful way.For example, in at least one embodiment, the trust rating system andmethod assigns the colors red, yellow and green to ranges of values toindicate High, Medium and Low values or segments. FIG. 10 displays anexample User Interface (UI) so that these segments are configured andassociated colors and descriptive terms are linked to represent theranges that can then be manifested in the user interface of theapplication.

In some cases, predicted data that is being measured fromtrustworthiness can be grouped into the segmentation ranges based on thehistorical Trust Factor. For example, in a forecasting system you mightfind a forecast number for a given time period, say a Quarter.Specifically you may have the forecast for Quarter 4, 2001 is 5,000,000US dollars. Now, this forecast is composed again of categories such as,but not limited to, customers, products and regions and historically,each of these components can be scored with a historical Trust Factorover time. Using this historically tracked Trust Factor you can thenlook at the revenue, in this case the $5M, and break the number up intosegments represented by the segmentation scheme defined. By example, youcould say that 25% of the $5M is historically “High Risk” or color “Red”based on the segmentation defined for it, while 50% is “Low Risk” orcolor “Green” based on that segmentation. In at least one embodiment,such data and associations are configurable and flexible in order tohandle a variety of scenarios.

Example System Architecture

In reference to a non-limiting example in FIG. 1, in order to satisfythe features and functionality the following is deemed as necessary toprovide the described solution. In at least one embodiment, predicteddata flow into the system either through the system itself (userinterface) or external providers of information. (101) Information iscollected into an OLTP database (database engine) organized in such away to capture the categories and also the predicted values as numericrepresentations (i.e. units, price, exchange rates, stock prices, etc.)(102) Periodically and based on the specific configuration for how Trustis to be calculated (trust factor calculation engine) as describedearlier, processing is done to calculate the Trust Factor ratings, thetrust element scores as well as the segmentation information. (103) Thisinformation is moved into an analytical system (analytical engine) suchas an OLAP repository for aggregation of the information along, forexample, the required categories or dimensions or hierarchies. (104) Abusiness logic layer or request layer is placed (logic engine) betweenthe user interface and analytical structures. This layer can handlerequests for trust information that is to be provided from theanalytical system. (105) Data is fed from the analytical system to theuser interface 106 through the business logic layer. The user interfaceis advantageously provided over a network of computers such as the webwith Internet delivery technology but may also be provided in aclient/server deliver model over a private network or over the Internet.

It may be appreciated that each of the afore mentioned engines mayinclude a processor or processing logic and memory logic coupled withthe processor or processing logic for implementing the function of thatengine as, for example, illustrated in FIG. 12.

Example Process Flow

FIG. 2 describes a non-limiting example of a typical process flow forhow Trust information or the trust factor may be calculated by the trustrating system and method as well how relevant source data is assembled.

In step or process 201, prediction data and outcome data is loaded intothe system as a prerequisite but it is not necessarily the case thatthey are be loaded at the same time. In at least one embodiment, outcomedata will lag the predicted data but at the time of Trust Factorcalculation both notions exist in the system.

At step or process 202 the trust rating system and method now extractsthe configured Trust Elements such as completeness and accuracy inpreparation to loop over each for processing.

In a loop each element's associated configuration information is loadedat step or process 203.

At step or process 204 the trust rating system and method determines ifthe element is enabled or used in the generation of the trust factoreither by a status indicator that may or may not be derived from whetheror not the element is weighted above 0.

At step or process 205 the trust rating system and method calculates theelement and normalize it against the configured scheme.

At step or process 206 the trust rating system and method appliesassociated segmentation information to the calculated value.

At step or process 207 the trust rating system and method determineswhether there are more elements to process. If so repeat, otherwise, thetrust rating system and method moves on.

At step or process 208 the trust rating system and method takes allconfigured elements and calculations and apply appropriate weighting toderive a Trust Factor.

At step or process 209 the trust rating system and method finallyaggregates up the associated normalized values along configured categoryhierarchies for analysis.

A non-limiting example of the trust rating system and method provides amethod and model for predicting or forecasting using the trust factor.

FIG. 12 is a block diagram illustrating a network environment in which atrust rating system and method may be practiced. For example, the trustrating system and method can receive data requests and provide data in,for example, web pages to allow trust factors to be displayed. Network1202 (e.g. a private wide area network (WAN) or the Internet) includes anumber of networked server computer systems 1204(1)-(N) that areaccessible by client computer systems 1206(1)-(N), where N is the numberof server computer systems connected to the network. Communicationbetween client computer systems 1206(1)-(N) and server computer systems1204(1)-(N) typically occurs over a network, such as a public switchedtelephone network over asynchronous digital subscriber line (ADSL)telephone lines or high-bandwidth trunks, for example communicationschannels providing T1 or OC3 service. Client computer systems1206(1)-(N) typically access server computer systems 1204(1)-(N) througha service provider, such as an internet service provider (“ISP”) byexecuting application specific software, commonly referred to as abrowser, on one of client computer systems 1206(1)-(N).

Client computer systems 1206(1)-(N) and/or server computer systems1204(1)-(N) may be, for example, computer systems of any appropriatedesign, including a mainframe, a mini-computer, a personal computersystem including notebook computers, a wireless, mobile computing device(including personal digital assistants). These computer systems aretypically information handling systems, which are designed to providecomputing power to one or more users, either locally or remotely. Such acomputer system may also include one or a plurality of input/output(“I/O”) devices coupled to the system processor to perform specializedfunctions. Mass storage devices such as hard disks, compact disk (“CD”)drives, digital versatile disk (“DVD”) drives, and magneto-opticaldrives may also be provided, either as an integrated or peripheraldevice. One such example computer system is shown in detail in FIG. 13.

Embodiments of the trust rating system and method can be implemented ona computer system such as a general-purpose computer 1300 illustrated inFIG. 13. Input user device(s) 1310, such as a keyboard and/or mouse, arecoupled to a bi-directional system bus 1318. The input user device(s)1310 are for introducing user input to the computer system andcommunicating that user input to processor 1313. The computer system ofFIG. 13 generally also includes a video memory 1314, main memory 1315and mass storage 1309, all coupled to bi-directional system bus 1318along with input user device(s) 1310 and processor 1313. The massstorage 1309 may include both fixed and removable media, such as otheravailable mass storage technology. Bus 1318 may contain, for example, 32address lines for addressing video memory 1314 or main memory 1315. Thesystem bus 1318 also includes, for example, an n-bit data bus fortransferring DATA between and among the components, such as CPU 1309,main memory 1315, video memory 1314 and mass storage 1309, where “n” is,for example, 32 or 64. Alternatively, multiplex data/address lines maybe used instead of separate data and address lines.

I/O device(s) 1319 may provide connections to peripheral devices, suchas a printer, and may also provide a direct connection to a remoteserver computer systems via a telephone link or to the Internet via anISP. I/O device(s) 1319 may also include a network interface device toprovide a direct connection to a remote server computer systems via adirect network link to the Internet via a POP (point of presence). Suchconnection may be made using, for example, wireless techniques,including digital cellular telephone connection, Cellular Digital PacketData (CDPD) connection, digital satellite data connection or the like.Examples of I/O devices include modems, sound and video devices, andspecialized communication devices such as the aforementioned networkinterface.

Computer programs and data are generally stored as instructions and datain mass storage 1309 until loaded into main memory 1315 for execution.Computer programs may also be in the form of electronic signalsmodulated in accordance with the computer program and data communicationtechnology when transferred via a network.

The processor 1313, in one embodiment, is a microprocessor manufacturedby Motorola Inc. of Illinois, Intel Corporation of California, orAdvanced Micro Devices of California. However, any other suitable singleor multiple microprocessors or microcomputers may be utilized. Mainmemory 1315 is comprised of dynamic random access memory (DRAM). Videomemory 1314 is a dual-ported video random access memory. One port of thevideo memory 1314 is coupled to video amplifier 1316. The videoamplifier 1316 is used to drive the display 1317. Video amplifier 1316is well known in the art and may be implemented by any suitable means.This circuitry converts pixel DATA stored in video memory 1314 to araster signal suitable for use by display 1317. Display 1317 is a typeof monitor suitable for displaying graphic images.

The computer system described above is for purposes of example only. Thetrust rating system and method may be implemented in any type ofcomputer system or programming or processing environment. It iscontemplated that the trust rating system and method might be run on astand-alone computer system, such as the one described above. The trustrating system and method might also be run from a server computersystems system that can be accessed by a plurality of client computersystems interconnected over an intranet network. Finally, the trustrating system and method may be run from a server computer system thatis accessible to clients over the Internet.

The trust rating system and method can be implemented as code stored ina non-transitory, tangible computer readable medium and executed by oneor more processors.

While the present invention has been described with reference to a fewspecific embodiments and examples, the description and the particularembodiments described are illustrative of the invention and are not tobe construed as limiting the invention. Various modifications may occurto those skilled in the art without departing from the true spirit andscope of the invention as defined by the description and the appendedclaims. All patents and publications referenced herein are herebyincorporated by reference.

1. A method for generating a trust factor indicator based on historicalperformance the method comprising the steps of: determining an objectivehistorically based prediction accuracy measure; determining an objectivehistorically based prediction bias measure; normalizing each of theaccuracy measure and bias measure to generate respective normalizedaccuracy measure and normalized bias measure; and combining thenormalized accuracy measure and normalized bias measure to generate atrust factor indicator.
 2. A method as in claim 1, wherein the combiningcomprises weighting the normalized accuracy measure by an accuracyweighting factor (W_(A)) and weighting the normalized bias measure by abias weighting factor (W_(B)) before combining
 3. A method as in claim1, wherein the combining further comprises an additive combination.
 4. Amethod as in claim 1, wherein the accuracy weighting factor (W_(A)) andthe bias weighting factor (W_(B)) are each different from zero (0) anddifferent from one (1).
 5. A method as in claim 1, wherein the weightingfactors are determined for each trust factor so that in combination thetotal weightings of the normalized values total to 1 or 100 percent sothat the trust factor is always a value between 0 and
 10. 6. A method asin claim 1, wherein the normalizing comprises applying an absolutecapped percentage scaling so that each of the measurements falls in apredetermined range (e.g. between 1 and 10).
 7. A method as in claim 1,wherein the normalizing comprises applying an unbounded positive andnegative percentage scaling so that each of the measurements falls in apredetermined range with positive and negative values represented by thevalues in the range (e.g. between +10 to −10).
 8. A method as in claim1, wherein the method further comprises determining a completenessmeasure and applying the completeness measure in combination with theaccuracy measure and the bias measure to generate the trust factor.
 9. Amethod as in claim 1, wherein the method further comprises determiningan attainment measure and applying the attainment measure in combinationwith the accuracy measure and the bias measure to generate the trustfactor.
 10. A method as in claim 1, wherein the method further comprisesdetermining a consistency measure and applying the consistency measurein combination with the accuracy measure and the bias measure togenerate the trust factor.
 11. A method as in claim 1, wherein the stepof determining an accuracy measure that represents a variance ordifference comparison of a future event predicted value (P) of anoutcome (value) to the actual event outcome (value) (O).
 12. A method asin claim 11, wherein the accuracy measure is unity (=1) (or a value thatcan be normalized to unity by a multiplier) when the predicted value Pis equal to the outcome value O and is less than one when the predictionvalue differs from the predicted value.
 13. A method as in claim 11,wherein the accuracy measure is a magnitude difference D and a predictedvalue that is greater than the outcome value by +D has the same effecton the accuracy measure as an equal magnitude difference −D.
 14. Amethod as in claim 11, wherein the accuracy is given by the expression:Accuracy=A=Function{P,O}.
 15. A method as in claim 11, wherein theaccuracy is given by the expression:Accuracy=1−ABS((P−O)/(P+O)), where P is prediction, O is outcome, andABS is the absolute value function.
 16. A method as in claim 1, whereinthe accuracy measure further includes an adjustment for an accuracytime-offset value, the accuracy time-offset value including a factorthat effectively increases the accuracy measure for an accurateprediction that is made a time period of t=t2 before the actual outcomerelative to an accurate prediction that was made a time period t=t1before the actual outcome, there the outcome is considered as occurringat t=t0 and t2>t1>t0.
 17. A method as in claim 1, wherein the accuracymeasure is given by the expression:Accuracy_(tov) =A _(tov)=Function{P _(tov) ,O) Where tov represents thetime-offset value.
 18. A method as in claim 1, wherein the accuracymeasure is given by the expression:Accuracy_(tov) =A _(tov)=1−ABS((P _(tov) −O)/(P _(tov) +O)), where tovrepresents the time-offset value.
 19. A method as in claim 1, whereinthe step of determining a bias measure that represents an objectivemeasure of a tendency of a forecast method or a received forecasterforecast input of a predicted outcome value to over-estimate the actualoutcome value or to under-estimate the actual outcome value.
 20. Amethod as in claim 19, wherein the bias measure is positive for a biasmeasure where a predicted value of the outcome value is greater than theactual outcome value, and the bias measure is negative for a biasmeasure where a predicted value of the outcome value is less than theactual outcome value.
 21. A method as in claim 19, wherein the biasmeasure is greater than a normalized value (e.g., 1 or 100) for a biasmeasure where a predicted value of the outcome value is greater than theactual outcome value, and the bias measure is less than one for a biasmeasure where a predicted value of the outcome value is less than theactual outcome value.
 22. A method as in claim 19, wherein the biasmeasure is an additive contribution to the trust factor.
 23. A method asin claim 19, wherein the bias measure is a multiplicative contributionto the trust factor.
 24. A method as in claim 19, wherein the biasmeasure is an exponential power contribution to the trust factor.
 25. Amethod as in claim 19, wherein the bias measurement includes a timeoffset value (tov) adjustment.
 26. A method as in claim 19, wherein thebias measure further includes an adjustment for a bias time-offsetvalue, the bias time-offset value including a factor that effectivelyincreases or decreases the bias measure for a prediction (accurateprediction that is made a time period of t=t2 before the actual outcomerelative to a prediction that was made a time period t=t1 before theactual outcome, there the outcome is considered as occurring at t=t0 andt2>t1>t0, or vice versa.
 27. A method as in claim 1, wherein the biasmeasure time-offset value is a linear function of time before the event.28. A method as in claim 27, wherein the bias measure time-offset valueis a non-linear function of time before the event.
 29. A method as inclaim 27, wherein the bias measure time-offset value is a function ofthe time to the end of a fiscal year.
 30. A method as in claim 27,wherein the bias measure time-offset value is a function of the time tothe end of a sales quarter.
 31. A method as in claim 27, wherein thebias measure time-offset value is a function of the time to the end of aforecaster bonus period or forecaster evaluation period.
 32. A method asin claim 27, wherein the bias measure time-offset value is a function ofthe time of year for that particular forecaster from whom the forecastwas received.
 33. A method as in claim 27, wherein the time-offset valuefor bias measure is only applied to the trust factor when the time-valueoffset is applied to the accuracy measure as a contribution to the trustfactor.
 34. A method as in claim 27, wherein the time-offset value forbias measure is applied to the trust factor independently of whether thetime-value offset is applied to the accuracy measure as a contributionto the trust factor.
 35. A method as in claim 27, wherein the biasmeasure is a multiplicative contribution to the trust factor.
 36. Amethod as in claim 27, wherein the bias measure is an additivecontribution to the trust factor.
 37. A method as in claim 27, whereinthe bias measure corrects for a historical bias in a forecast by aforecaster.
 38. A method as in claim 1, wherein the step of determininga forecast activity completeness measure that represents an objectivemeasure of a forecast method or a received forecaster activity relativeto forecasts made for which an outcome corresponding to the forecast isstill pending, and wherein an activity includes one or a combination of(i) reviewing a prior forecast value but leaving it unchanged to therebyvalidate that it is still a valid prediction, (ii) reviewing a priorforecast value and changing it to reflect an updated or revisedprediction to the earlier prior forecast.
 39. A method as in claim 38,wherein the completeness measure represents a ratio or factor (orpercentage) of the number of pending forecasts that have been validatedas compared to the total number of pending forecasts for which theoutcome has not yet occurred, where validation is accomplished either byreviewing the forecast and leaving it unchanged or by changing theforecast with implied validation at that point in time.
 40. A method asin claim 38, wherein the completeness measure is a function of the ratioof a number of pending forecasts that are validated divided by a totalnumber of pending forecasts as follows:Completeness=Constant×(number of pending forecasts that arevalidated/total number of pending forecasts).
 41. A method as in claim38, wherein the completeness measure further includes a time-offsetvalue compensation.
 42. A method as in claim 38, wherein thecompleteness measure is computed for a defined time period prior to theoutcome.
 43. A method as in claim 38, wherein the time period is a timeperiod for a one-month period preceding the calculation of thecompleteness measure.
 44. A method as in claim 38, wherein the timeperiod is a time period for the calendar month preceding the calculationof the completeness measure.
 45. A method as in claim 38, wherein thetime period is a time period for the calendar quarter preceding thecalculation of the completeness measure.
 46. A method as in claim 38,wherein the time period is a time period for a specified number of days,weeks, or months preceding the calculation of the completeness measure.47. A method as in claim 38, wherein the completeness is modified by atime-offset value.
 48. A method as in claim 1, wherein the step ofdetermining an attainment measure that represents an objective measureof how much the predicted outcome value either fell above (exceeded) orfell below (deceeded) the actual outcome value for a forecast, and isdetermined after the forecasted event has occurred and the outcome valueis known.
 49. A method as in claim 48, wherein the attainment measure isdetermined based on the expression:Attainment=Function{P,O}, where P is prediction of the outcome, O is theactual outcome.
 50. A method as in claim 48, wherein the attainmentmeasure is determined based on the expression:Attainment=(O/P)×10, where P is prediction, O is outcome, and 10 is ascaling or weighting factor (scaling may be any integer or real number).51. A method as in claim 48, wherein the attainment measure is modifiedto include a time-offset value that provides for a higher attainmentmeasure as compared to lower attainment values the further in advance ofthe determined based on the expression:Attainment=Function{P _(tov) ,O}, where P is prediction of the outcome,tov represents the time-offset value, and O is the actual outcome.
 52. Amethod as in claim 48, wherein the attainment measure is modified toinclude a time-offset value that provides for a higher attainmentmeasure for earlier accurately predicted outcomes as compared toattainment values for later accurately predicted outcome values based onthe expression:Attainment=(O/P _(tov))×10, where P is prediction, O is outcome and tovrepresents the time-offset value.
 53. A method as in claim 48, whereinattainment measure is an indication of how frequently the forecastpredictions are checked for a set of predictions.
 54. A method as inclaim 48, wherein the attainment is a different measure than theaccuracy measure even though both accuracy and attainment measures havea comparison between the predicted value and the actual outcome value.55. A method as in claim 1, wherein the step of determining aconsistency measure that represents an objective measure of howconsistently a forecast method or forecaster predicted an outcome valueover a period of time of over a set of spaced apart times.
 56. A methodas in claim 55, wherein the consistency is calculated as a standarddeviation of each of the predicted outcomes or attainment measures ascompared with a reference outcome or reference attainment.
 57. A methodas in claim 55, wherein the consistency is calculated according to theexpression:Consistency=Function{attainment,mean value of attainment}
 58. A methodas in claim 55, wherein the consistency is calculated according to theexpression:Consistency=Sigma=SQRT{[(attainment₁−mean value of attainment)²+ . . .+(attainment_(N)−mean value of attainment)²]/N}
 59. A method as in claim55, wherein the standard deviation is the square root of its varianceand indicates how much variation there is from the average (or mean)value of the outcome or of the attainment measure.
 60. A method as inclaim 1, wherein the method further comprises including a trendingelement in the generation of the trust factor.
 61. A method as in claim1, wherein the method further comprises including an aggregation intothe generation of the trust factor and using this in a trust analysis.62. A method as in claim 1, wherein the method further comprisesincluding a segmentation into the generation of the trust factor andusing this in a trust analysis.
 63. A method comprising: (A) receivingat least one initial future forecast prediction data from an externalforecaster for a future event or condition that has not yet occurred;(B) communicating the at least one initial future forecast predictiondata to a first level manager, and optionally communicating any changesto the at least one initial future forecast prediction data to the firstlevel manager, the first level manager being at a higher responsibilityrelative to the forecaster and an executive level manager at a higherresponsibility relative to the first level manager; (C) reviewing the atleast one initial future forecast prediction data; (D) overlaying atrust factor information and associated elements or components; (E)analyzing the at least one initial future forecast prediction data andany changes to the initial future forecast prediction data to identifyvalues, predictions, and/or forecasts that have a trust factor scorethat has a magnitude relationship relative to a predetermined trustfactor score that indicate a forecast risk in the future forecastprediction data; (F) determining if the identified risk is greater thanor less than a risk threshold, the risk threshold optionally beingadjusted or dynamically determined based on an impact on an associatedperformance metric; (G) if the forecast risk is less than the riskthreshold in view of the impact of an inaccurate forecast, thenapproving the forecast and communicating the forecast to an executivemanager for further consideration, but if the risk is greater than therisk threshold in view of the impact of an inaccurate forecast, theneither (i) sending the forecast back to the forecaster with first levelmanager feedback comments and a request that the forecast be updatedbased on the first level manager feedback; or (ii) the first levelmanager updating/revising the forecast based on the first levelmanager's judgment; (H) when the forecast is sent back to the forecasterfor update/revision, receiving an updated or revised forecast inresponse to the first level manager feedback and request forupdate/revision; (I) when the forecast is updated/revised by the firstlevel manager, receiving an updated or revised forecast from the firstlevel manager, the updated/revised forecast being either a change in theforecast or a comment attached to the forecast to explain/justify theforecast and the risk, or a combination of a change in the forecast anda comment attached to the forecast to explain/justify the forecast; (J)iteratively revising the forecast by the forecaster and/or the firstlevel manager until the risk based on trust is determined to be withinacceptable limits, and then communicating the forecast including anyupdates/revisions to the forecast to the executive manager, thecommunicating of the trust factor reviewed and possibly updated/revisedforecast to the executive manager representing a commitment of the firstlevel manager to the forecast; (K) analyzing the committed forecastusing the trust factor, including reviewing the forecast value(s), thetrust information, and the identified risk; (L) determining that thecommitted forecast is unacceptable for a reason, the reason being one ora combination of two or more selected from (i) the forecast is notwithin an acceptable forecast value range for the forecast event, (ii)the risk profile is unacceptable; (M) if the committed forecast from thefirst level manager is unacceptable, then communicating the forecastwith executive feedback including executive manager feedback comments,to the first level manager for updating/revision by the first levelmanager and/or for updating/revision by the forecaster; and (N) if thecommitted forecast from the first level manager is acceptable thenidentifying the executive manager approved forecast as the finalforecast that has passed the trust factor analysis with acceptable trustrisk and acceptable performance metric.